Next Session (Fall 2026)

Monday, November 9, 2026 at USC. Talks are in the Kaprielian Hall, KAP 414. 

3:00-3:30 pm: Informal Meet & Greet in the Kaprielian Hall, KAP 410.

3:30–4:20 p.m.: Steven Kou (Questrom School of Business, Boston University)

Non-Standard Dynamic Utility Maximization

This talk will survey recent research on non-standard dynamic utility maximization, where the utility function may not be concave or increasing. Examples include (1) payoffs option-type managerial compensation or from equity-linked life insurance contracts, (2) nonconcave utility functions used in behavioral economics,  (3) the goal problems in household finance, (4) dynamic mean-variance analysis, and (5) median and quantile maximization. The latter two also have time inconsistency issues.

4:30–5:20 p.m.: Ludovic Tangpi (Princeton University)

The convergence problem for ergodic mean field games

We discuss ergodic N-player games and their mean field limits in a setting of controlled common noise and non-linear dynamics. Assuming that the game is displacement monotone, we derive a quantitative convergence result, which demonstrates that as the number of players increases, the open-loop Nash equilibria in Markovian form of the N-player game converge to the MFE. Along the way, we also provide a general existence result for mean field equilibria. The work is based on a join work with Kaiwen Zhang.

5:30–5:50 p.m.: Xihao He (USC)

Sharp functional quantization and empirical Wasserstein rates for Itô processes

We establish functional quantization and empirical Wasserstein rates for continuous Itô processes under the supremum norm. We assume that the initial condition and the drift and diffusion integrands are controlled by a time-uniform random upper bound with a finite $\rho$-moment for some $\rho > 1$. Under this assumption, the $n$-point $L^q$ quantization error is at most $C(\log n)^{-1/2}$ for every $1 \leq q < \rho$. The known Brownian lower bound shows that the exponent $1/2$ is optimal over this class. Our proof combines an adaptive dyadic time partition with localization according to the size of the integrands. A general transfer principle yields the sharp mean rate $(\log N)^{-1/2}$ for the $p$-Wasserstein distance between the empirical law of $N$ independent copies and their common path law, together with nonasymptotic deviation bounds, whenever $1 \leq p < \rho$. Applications include empirical path-law estimates for path-dependent SDEs and a path-space limit-theory estimate for path-dependent McKean–Vlasov interacting particle systems with common noise.

6:00–6:20 p.m.: Brendan Martin (UCLA)

Spectral methods for recovering latent structure in network time series

High-dimensional financial time series often exhibit complex dependence relations driven by both common market structures and latent connections among assets. To capture these characteristics, we develop methodology for the common and idiosyncratic components of high-dimensional time series with an underlying unobserved network structure. The common component is modelled by a static factor model, which allows for strong cross-sectional dependence, whilst a network vector autoregressive process captures the residual co-movements due to the idiosyncratic component. An assortative stochastic block model underlies the network VAR, leading to groups of highly co-moving variables in the idiosyncratic component. We propose an estimation framework based on spectral clustering of the sample covariance matrix and derive non-asymptotic convergence rates which depend explicitly on the network size, sample length, community separation, and degree of data dependence. The method is demonstrated in financial applications to daily returns, intraday returns, and FRED-MD macroeconomic variables.

Dinner: Thai By Trio, 2700 S. Figueroa St., Los Angeles, CA 90007.